The decision-maker's observing policy must be timely

The decision-maker's observing policy must be timely

Previous work has focused mainly on bounding either the expected loss of a predictor or the probability that an individual prediction will incur a loss value in a specified range.

OS-VI achieves a much faster convergence rate when the model is accurate enough.

Clarke stationary points are global minimum.

In fact, our algorithm enjoys sublinear adaptive regret bounds, which is a strictly stronger metric than standard regret and is more appropriate for time-varying systems.

Learning from previously collected data via behavioral cloning or offline reinforcement learning (RL) is a powerful recipe for scaling generalist agents by avoiding the need for expensive online learning. Despite strong generalization in some respects, agents are often remarkably brittle to minor visual variations in control-irrelevant factors such as the background or camera viewpoint. In this paper, we present theDeepMind Control Visual Benchmark (DMC-VB), a dataset collected in the DeepMind Control Suite to evaluate the robustness of offline RL agents for solving continuous control tasks from visual input in the presence of visual distractors. In contrast to prior works, our dataset (a) combines locomotion and navigation tasks of varying difficulties, (b) includes static and dynamic visual variations, (c) considers data generated by policies with different skill levels, (d) systematically returns pairs of state and pixel observation, (e) is an order of magnitude larger, and (f) includes tasks with hidden goals. Accompanying our dataset, we propose three benchmarks to evaluate representation learning methods for pretraining, and carry out experiments on several recently proposed methods.

Taking advantage of their versatility and autonomous operation, unmanned aerial vehicles (UAVs) can be used for aerial load transportation, with many applications such as vertical replenishment of seaborne vessels [11], deployment of supplies in search-and-rescue missions [1], package delivery, and landmine detection [2]. Aerial load transportation using UA Vs is a challenging task in terms of modeling and control. The load may be connected to the UAV either rigidly or by means of a rope, which changes its dynamics considerably. In addition, the load physical parameters are often unknown in practice, and their knowledge is usually necessary to effectively accomplish the task. A model-free control approach based on trajectory generation by reinforcement learning has been proposed in [7] for path tracking of the load using a quadrotor UAV (QUAV). This work was in part supported by the project INCT (National Institute of Science and Technology) for Cooperative Autonomous Systems Applied to Security and Environment under the grants CNPq 465755/2014-3 and F APESP 2014/50851-0, and by the Brazilian agencies CAPES under the grant numbers 88887.136349/2017-00